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Desirability of Nominal GDP Targeting Under Adaptive Learning

  • Kaushik Mitra

Nominal GDP targeting has been advocated by a number of authors since it produces relative stability of inflation and output. However, all of the papers assume rational expectations on the part of private agents. In this paper I provide an analysis of this assumption. I use stability under recursive learning as a criterion for evaluating nominal GDP targeting in the context of a model with explicit micro- foundations which is currently the workhorse for the analysis of monetary policy. The results of the paper provide support for such a monetary policy.

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Paper provided by Department of Economics, University of York in its series Discussion Papers with number 00/60.

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Handle: RePEc:yor:yorken:00/60
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